Table of contents
- 0. Review of Algebra4h 16m
- 1. Equations & Inequalities3h 18m
- 2. Graphs of Equations43m
- 3. Functions2h 17m
- 4. Polynomial Functions1h 44m
- 5. Rational Functions1h 23m
- 6. Exponential & Logarithmic Functions2h 28m
- 7. Systems of Equations & Matrices4h 6m
- 8. Conic Sections2h 23m
- 9. Sequences, Series, & Induction1h 19m
- 10. Combinatorics & Probability1h 45m
7. Systems of Equations & Matrices
Graphing Systems of Inequalities
19:09 minutes
Problem 13b
Textbook Question
Textbook QuestionIn Exercises 5–14, an objective function and a system of linear inequalities representing constraints are given. a. Graph the system of inequalities representing the constraints. b. Find the value of the objective function at each corner of the graphed region. c. Use the values in part (b) to determine the maximum value of the objective function and the values of x and y for which the maximum occurs.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Linear Inequalities
Linear inequalities are mathematical expressions that involve a linear function and an inequality sign (such as <, >, ≤, or ≥). They represent regions on a graph where the solutions satisfy the inequality. Understanding how to graph these inequalities is crucial, as it helps visualize the feasible region defined by the constraints in optimization problems.
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Objective Function
An objective function is a mathematical expression that defines the goal of an optimization problem, typically to maximize or minimize a certain quantity. In this context, it is a function of variables (like x and y) that needs to be evaluated at various points, particularly at the vertices of the feasible region, to find the optimal solution.
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Corner Point Theorem
The Corner Point Theorem states that in a linear programming problem, the maximum or minimum value of the objective function occurs at one of the vertices (corner points) of the feasible region. This theorem is essential for solving optimization problems, as it simplifies the process of finding the optimal solution by limiting the evaluation to these critical points.
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